Tag Archives: IoT

I try and fit components together logically so that they can make the most of what the technology offers. I work predominantly in the OSS world on new access technologies like 5G and implementations like the Internet of Things. I want to achieve not just the deployment of these capabilities but to also to let them operate seamlessly. The following is my view of the opportunity of closed-loop remediation.

For closed-loop remediation there are two main tenets: 1. you can stream all network event data in a machine learning engine and apply an algorithm like K-Nearest Neighbour 2. you can expose remediation APIs on your programmable network.

All of this requires a lot of technology convergence but: What’s actually needed to make everything convergent?

Let’s start with Streaming. Traditionally we used SNMP for event data, traps & alarms and when that didn’t work we deployed physical network probes. Now it’s Kafka stream once implementations where a streams of logs of virtualised infrastructure and virtualised functions are parsed in a data streaming architecture into different big data persistence.

The Machine Learning engine, I’m keenest of FlinkML at the moment, works on the big data persistence providing the largest possible corpus of event data. The ML K-NN can analyse network behaviour and examine patterns that are harder for human operation teams to spot. It can also predict timed usage behaviours and scale the network accordingly.

I am increasingly looking at Openstack and Open Source Mano as a NFVO platform orchestrating available virtualised network functions. The NFVO can expose a customer facing service or underlying RFSs. But to truly operate the ML should have access to the RFS layer. This is the hardest part and is dependent upon the underlying design pattern implementation of the Virtual Network Functions. This though is a topic for another blog post.

Mobile Edge Computing (MEC) is a key piece of the 5G architecture (or 5G type claims on a 4G RAN). MEC can already make a huge difference in video latency and quality for video streaming multiple feeds within a sporting environment. For example Intel, Nokia and China Mobile video streams of the Grand Prix at Shanghai International Circuit.

A 5G mobile operator will be introducing virtualised network functions as well as mobile edge computing infrastructure. This creates both opportunities and challenges. The opportunities are the major MEC use cases included context-aware services, localised content and computation, low latency services, in-building use cases and venue revenue uplift.

The challenges include providing the Mobile Edge Compute Platform in a virtualised 5G world. Mobile operators are not normally IaaS / PaaS providers so this may become a challenge.

The ETSI 2018 group report Deployment of Mobile Edge Computing in an NFV environment describes an architecture based on a virtualised Mobile Edge Platform and a Mobile Edge Platform Manager (MEPM-V). The Mobile Edge Platform runs on NFVI managed by a VIM. This in turn hosts the MEC applications.

The ETSI architecture seems perfectly logical and reuses the NFVO and NFVI components familiar to all virtualisations. In this architecture the NFVO and MEPM-V act as what ETSI calls the Mobile Edge Application Orchestrator” (MEAO) for managing MEC applications. The MEAO uses NFVO for resource orchestration and for the element manager orchestration.

The difficulty still lies in implementing the appropriate technologies to suit the MEC use cases. Openstack (or others) may provide the NFVI and Open Source Mano (or others) may provide the NFVO; however what doesn’t exist is the service exposure, image management and software promotion necessary for a company to on-board MEC.

If MEC does take off what is the likelihood that AWS, GCP and Azure will extend their footprint into the telecom operators edge?

The Internet of Things requires multiple Reference Architectures which can map capabilities to specific technology domains. This is a challenge because there is no single unifying industry definition for the Internet of Things. For the purpose of this presentation it is assumed that:

“Things” have semantic representation in the Internet

“Things” can be acted upon in a structured manner (e.g., status, capabilities, location, measurements) or can report in structured data or can communicate directly with other “Things”

There are many different usable protocols for communication with M2M devices for the Internet of Things. Specific protocols are more appropriate for different devices (e.g. memory & power profiles) and specific protocols are more appropriate for different communication needs (e.g. State Transfer Model & Event Based Model)

The Internet of Things is not predicated on mobile or fixed-line operators. It is predicated on the value derived from the interplay between different sensors and actuators. In the history of mobile telecommunications it was the mobile network operators who provided a service that brought together radio waves and handset manufacturers. The success of mobile telecommunications has led to a 93.5% global saturation rate (source Informa) with the conglomerate operators China Mobile Vodafone. Airtel and Verizon etc being the big winners.

The Internet of Things, as distinct from the internet of people, requires communication between devices to enable home automation, telematics and health care monitoring. This intercommunication is dependent upon semantically structured and shared data for enabling functions such as identification, authentication, authorisation, bootstrapping and provisioning. Standardising both the semantically structured data and the enabling functions across M2M applications and devices would reduce the cost and extend the life of M2M devices. Standardisation for the Internet of Things is the aim of a common service layer for M2M.

There is no single internet definition for the Internet of Things, but to function as a network “Things” must have representation within the Internet. This representation includes structured data (e.g., status, capabilities, location, measurements) which needs to be semantic in order to be shared, processed and acted upon. The sharing and discoverability of information requires governance according to privacy settings and access rights. Because the Internet of Things (IoT) requires M2M application and device communication within minimal or no human involvement these privacy settings and access rights need to represent the delegated authority of the human user. Therefore M2M devices require bootstrapping of provisioned M2M service credentials (e.g. identities, M2M Root Key) which can be used for connecting and registering with different service layers and service authorities.

M2M devices can be active such as Zigbee sensors or passive such as RFID tags. M2M devices can be connected to an MNOs network using eUICCs or can be connected to an IP extending personal area network (e.g. Zigbee gateway). The ETSI principles for M2M & smart device communication are RESTful resource oriented APIs that are IP based but interwork with specific IP and non IP technologies in the M2M Area networks. The variance between device communication mechanism (IP & non-IP) and behaviour (passive vs active) makes defining an embeddable M2M common service layer a challenge.

The oneM2M group aims to develop technical specifications that address the need for a common M2M Service Layer that can be readily embedded within various hardware and software, and relied upon to connect the myriad of devices in the field with M2M application servers worldwide. The common M2M Service Layer should be agnostic to underlying network technology (yet leveraging the unique features of these underlying networks), and it will use network layer services (such as security (encryption and authentication), QoS, policy, provisioning, etc.) through an adaptation layer/APIs.

The challenge for Authentication, Authorisation & Accounting in M2M will be to build trust between the human user and the delegated authority to the M2M device. In order for an embedded common M2M service layer to operate it must support AAA (authN, authZ & accounting) for smart devices using an agreeable mechanism between multiple device manufacturers and network operators. The Telecommunications Industry Association are defining a functional standard for Authentication, Authorization and Accounting for Smart Device (AAA-SD TIA) which is encapsulated in TIA TR-50 Functional architecture for M2M Smart Device Communication System Architecture describes AAA-SD as ” provide authentication, authorisation and accounting services to other entities in the network to establish and enforce security policies. The services may include generation of keys, generation and validation of certificates, validation of signatures, etc”. The functions proposed by the oneM2M service layer must align with TIA TR-50.